The execution layer for autonomous AI agents. Orchestrate, observe, and optimize multi-agent workflows in production — without the infrastructure tax.
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[ 01 ]Core capabilities
Zero-Cold-Start Orchestration
Agents wake up with full context. No bootstrap latency, no state hydration — just immediate execution. Runtime pre-bakes agent graphs so the first inference is as fast as the thousandth.
Deterministic Execution Graphs
Every agent run produces a verifiable DAG. Inspect, replay, and branch from any node. Debug production failures in seconds, not hours.
Multi-Model Routing
Route tasks to the right model automatically. Expensive reasoning only when needed, fast inference for everything else. Cut costs by 60% without cutting corners.
[ 02 ]In the loop
Pre-baked graphs mean no bootstrap latency. Deterministic DAGs mean every run is inspectable, replayable, branchable. Routing means the expensive model only fires when the task earns it.
$ mynd run --graph triage --env prod
✓ graph warm — zero cold start, full context
✓ routed — fast inference ×3, reasoning ×1
✓ dag verified — replay from any node
✓ cost — −60% vs single-model baseline
p99: 47ms_
[ 03 ]By the numbers
[ a ]P99 orchestration latency
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[ b ]Runtime uptime SLA
[ c ]Agent runs per month
[ d ]Avg. cost reduction
[ the division of labor ]
Runtime handles orchestration,
observability, and optimization.
You handle the logic.
[ 04 ]Get started
Runtime handles orchestration, observability, and optimization. You handle the logic.